It is tempting to feed a random point from the representation space to the
autoencoder's decoder, and observe how much sense it makes of that point.
However, because one cannot control the shape of the
distribution of melody representations, one cannot guarantee
that a given point from the representation space would be similar to
those seen by the decoder during training.
Thus, the vanilla autoencoder architecture [1]} is not viable as a generative model.
We propose the following improvements for generating melodies:
